Name
Abstract | ||
|---|---|---|
•A novel domain adaptation method, named challenging tough sample networks (CTSN), is proposed to challenge tough samples in the target domain.•We report that leveraging the labels of easy target samples can ideally convert an unsupervised domain adaptation problem to a semi-supervised one.•An algorithm for tough sample identification is developed. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.patcog.2020.107540 | Pattern Recognition |
Keywords | DocType | Volume |
Domain adaptation,transfer learning,adversarial learning | Journal | 110 |
Issue | ISSN | Citations |
1 | 0031-3203 | 0 |
PageRank | References | Authors |
0.34 | 32 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lin Zuo | 1 | 3 | 1.04 |
| Mengmeng Jing | 2 | 73 | 4.72 |
| Jingjing Li | 3 | 597 | 44.26 |
| Lei Zhu | 4 | 854 | 51.69 |
| Ke Lu | 5 | 279 | 18.85 |
| Yang Yang | 6 | 332 | 20.67 |